Rana Rajib, Higgins Niall, Haque Kazi Nazmul, Burke Kylie, Turner Kathryn, Stedman Terry
School of Mathematics, Physics and Computing, Springfield Campus, University of Southern Queensland, Springfield Education City, QLD 4300, Australia.
Mental Health and Specialist Services, West Moreton Health, Brisbane, QLD 4076, Australia.
Nurs Rep. 2024 Dec 20;14(4):4162-4172. doi: 10.3390/nursrep14040303.
Optimum efficiency and responsiveness to callers of mental health helplines can only be achieved if call priority is accurately identified. Currently, call operators making a triage assessment rely heavily on their clinical judgment and experience. Due to the significant morbidity and mortality associated with mental illness, there is an urgent need to identify callers to helplines who have a high level of distress and need to be seen by a clinician who can offer interventions for treatment. This study delves into the potential of using machine learning (ML) to estimate call priority from the properties of the callers' voices rather than evaluating the spoken words.
Phone callers' speech is first isolated using existing APIs, then features or representations are extracted from the raw speech. These are then fed into a series of deep learning neural networks to classify priority level from the audio representation.
Development of a deep learning neural network architecture that instantly determines positive and negative levels in the input speech segments. A total of 459 call records from a mental health helpline were investigated. The final ML model achieved a balanced accuracy of 92% correct identification of both positive and negative instances of call priority.
The priority level provides an estimate of voice quality in terms of positive or negative demeanor that can be simultaneously displayed using a web interface on a computer or smartphone.
只有准确识别呼叫优先级，才能实现心理健康热线对来电者的最佳效率和响应能力。目前，进行分诊评估的热线接线员严重依赖其临床判断和经验。由于精神疾病会导致较高的发病率和死亡率，因此迫切需要识别出那些极度痛苦、需要由能够提供治疗干预的临床医生诊治的热线来电者。本研究深入探讨了使用机器学习（ML）从来电者声音特征而非所讲内容来估计呼叫优先级的潜力。
首先使用现有应用程序编程接口（API）分离来电者的语音，然后从原始语音中提取特征或表征。接着将这些特征输入一系列深度学习神经网络，以便根据音频表征对优先级进行分类。
开发出一种深度学习神经网络架构，可立即确定输入语音片段中的积极和消极程度。共调查了来自一条心理健康热线的459条通话记录。最终的机器学习模型在正确识别呼叫优先级的正例和负例方面，达到了92%的平衡准确率。
优先级水平提供了一种基于积极或消极态度的语音质量评估，可通过计算机或智能手机上的网络界面同时显示。
